Review of Indicators of Smart Cities

In a seminal work by Hensher and Puckett (2005), the importance of buyer-supplier interactions to reduce freight transportation costs in a city was emphasized. The authors also emphasized the need for logistics performance among several agents of the supply chain rather than each focusing solely on profitability. Performance of logistics is a function of reduction in traffic congestion, which is the major issue in city logistics. This issue can be overcome by better interactions between different supply chain levels. Similarly, Yu and Liao (2008) have explored the role of governance in the integration of local suppliers with manufacturing firms. A comparison was done between local and international firms and their transactions with local suppliers in China. Their study elaborates the role of government in encouraging investment in smart cities. The role of government to design its policies to facilitate transaction-specific investments and long-term relationships with international manufacturers is emphasized. Lombardi et al. (2012) modeled the performance of smart cities' carbon emissions as an important smart city component. In the same year, Russo and Comi (2012) identified several indicators for designing and assessing city logistics based on their analysis of several European cities. These indicators include traffic congestion, mobility, safety, noise, and pollutants emitted into the environment. Sustainability performance is measured using these indicators. The study revealed that high cost investments are not necessary to achieve environmental goals in the cities. Recently, Jong et al. (2015) also identified sustainability as an important indicator for urban development, policy-making, and business practices in smart cities. A variety of terms related to smart cities are studied by authors, such as sustainable city, smart city, resilient city, low-carbon city, eco city, and knowledge city. Jong et al. observed in their study that all of these terms are used interchangeably, and that the key objective of these cities is to establish an optimal balance between economic, social, and environmental activities. Therefore, there is a need to address carbon calculations in city logistics to make a city smart. Anand et al. (2015) also reviewed several city logistics models and evaluated their environmental, economic, and safety aspects. It was observed that delivery vehicles were major sources of freight problems. Therefore, the choice and design of vehicles must be also addressed while modeling city logistics. The following section provides a focused review of city logistics models for smart cities.